AI & ML
DeepRadio: A Human-Agent Workspace for Physical Radio Development
arXiv cs.HC0 views
arXiv:2611.09418v1 Announce Type: new Abstract: Agentic programming can automate software-defined radio (SDR) development, but decisions can propagate from software to hardware without sufficient user visibility or control. We present DeepRadio, a human-agent workspace embedded in GNU Radio Companion (GRC) for jointly constructing, verifying, and operating physical radio systems. DeepRadio uses a dynamic and state-driven workflow to coordinate a traceable radio specification, claim-oriented cross-layer verification, and mixed-initiative control. These mechanisms expose decisions, tie conclusions to available evidence, and return control to users before consequential hardware actions. Across seven atomic SDR tasks, DeepRadio completed nearly all runs while satisfying all prescribed software and radio-frequency constraints. In a 20-participant study, more participants achieved a verified physical deployment with DeepRadio than with Codex-assisted GRC. Participants reported stronger support for shared intent and understanding while retaining initiative and control. These results suggest that DeepRadio can make agent-assisted physical radio development more reliable, understandable, and controllable.
Source:https://arxiv.org/abs/2611.09418PreviousSensed but Not Told: Hidden Physiological Adaptation Shapes LLM PersuasionNextEntering a New Domain in the Era of Generative AI: An Autoethnography of Solo Game Development as a Bioinformatics Educator
Related posts
What free AI training in Nepal can teach you before you pay for a class
0dev.to

Agents draft. People decide.
0dev.to UX

โค้ดเพิ่ม 400% แต่คุณไม่ได้เร็วขึ้น 400%: GitHub ชี้คอขวดใหม่อยู่ที่การรีวิว
0dev.to GitHub

Drago: Your GitHub Dragon
0dev.to GitHub
Case Study: Automating Tableau to Power BI Migration with Up to 90% Accuracy.
0dev.to Data
Vom Bluttest ins Molekül: Warum die Alamar/Nightingale-Studie räumliches Denken braucht...
0dev.to Data